GCP AutoML SLA Credits & Refunds

Google's AI/ML SLAs — AutoML included — are written around "Monthly Uptime Percentage" with credit tiers that step up as availability drops. This guide explains the AutoML SLA in plain terms, calls out the exclusions that most often defeat claims, and shows how automated monitoring sidesteps the manual evidence problem.

What this guide covers

• The official GCP AutoML uptime commitment and credit tiers
• Which incidents qualify (and which exclusions silently disqualify claims)
• How to file an AutoML credit request inside the GCP claim window
• Why manual claim recovery typically leaves money on the table

Frequently asked questions about GCP AutoML SLAs

What is the typical SLA uptime guarantee for GCP AutoML?

AutoML has been folded into Vertex AI, and its prediction and training endpoints inherit the corresponding Vertex AI commitments — Google's published monthly uptime target is 99.9% for AutoML serving (online prediction) where covered. If Google fails to meet this commitment during a billing cycle, you are eligible to receive a portion of your AutoML spend back as a service credit.

How do I claim GCP AutoML SLA credits after an outage?

File a Financial Credit Request through Google Cloud Support within 30 days of the end of the affected billing month — the deadline is shorter than AWS or Azure, which catches a lot of teams out. Include your Project ID, the affected AutoML resources, downtime intervals (with timezone), supporting evidence from Cloud Monitoring or your own observability stack, and a calculation showing where Monthly Uptime Percentage fell below the SLA threshold. Google issues approved credits against your billing account, not as cash refunds.

What exclusions apply to the GCP AutoML SLA?

Specifically for AutoML, training jobs are explicitly excluded from the uptime SLA — only deployed prediction endpoints are covered, and errors caused by model size, quota exhaustion, or unsupported input formats do not qualify as covered downtime.

Why is it difficult to get refunds for AutoML outages manually?

AI/ML SLAs are still maturing, and AutoML carries some of the most nuanced terms in the cloud catalog. Rate limits, queue depths, and model availability all get measured differently, and the SLA often excludes throttling that the provider deems "expected." Teams that successfully claim AutoML credits do so by capturing per-request latency and error-code data and matching it precisely against the published terms.

Related GCP SLA guides

Other Google Cloud services with their own published SLA and 30-day claim window:

• GCP Vertex AI SLA credits — AI/ML
• GCP Cloud Vision SLA credits — AI/ML
• GCP Compute Engine SLA credits — Compute
• GCP Cloud Storage SLA credits — Storage

Don't miss GCP's 30-day claim window

GCP's claim deadline for AutoML is the shortest of the three major clouds, and most teams miss it for the same reason: nobody owns "file SLA credit requests" as a recurring task. By the time finance closes out the month, the window is already gone.

Next Signal monitors AutoML availability, files the Financial Credit Request inside Google's deadline, and tracks the claim through resolution. See how it works or start a free trial.